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Machine Learning with Python

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Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho

Network:Machine Learning with Python India4 598 Education2 400...

📈 Analytical overview of Telegram channel Machine Learning with Python

Channel Machine Learning with Python (@codeprogrammer) in the English language segment is an active participant. Currently, the community unites 68 471 subscribers, ranking 2 400 in the Education category and 4 598 in the India region.

📊 Audience metrics and dynamics

Since its creation on невідомо, the project has demonstrated rapid growth, gathering an audience of 68 471 subscribers.

According to the latest data from 31 October, 2026, the channel demonstrates stable activity. Although there has been a change in the number of participants by 12 over the last 30 days and by -24 over the last 24 hours, overall reach remains high.

Verification status: Not verified
Engagement rate (ER): The average audience engagement rate is 3.99%. Within the first 24 hours after publication, content typically collects 1.46% reactions from the total number of subscribers.
Post reach: On average, each post receives 2 735 views. Within the first day, a publication typically gains 998 views.
Reactions and interaction: The audience actively supports content: the average number of reactions per post is 8.
Thematic interests: Content is focused on key topics such as insidead, learning, degree, evaluation, algorithm.

📝 Description and content policy

The author describes the resource as a platform for expressing subjective opinions:
“Learn Machine Learning with hands-on Python tutorials, real-world code examples, and clear explanations for researchers and developers. Admin: @HusseinSheikho || @Hussein_Sheikho”

Thanks to the high frequency of updates (latest data received on 01 November, 2026), the channel maintains relevance and a high level of publication reach. Analytics show that the audience actively interacts with content, making it an important point of influence in the Education category.

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68 471

Subscribers

-2424 hours

-947 days

+1230 days

2 735

Post views

~ 99824 hours

~ 1 36748 hours

3.99%

Engagement rate

~ 1

Posts per day

Ads index

beta

Posts Archive

68 469

Repost from Machine Learning

📌 Extracting Structured Vehicle Data from Images 🗂 Category: 🕒 Date: 2025-01-27 | ⏱️ Read time: 10 min read Build an Automated Vehicle Documentation System that Extracts Structured Information from Images, using OpenAI API,…

68 469

Repost from Machine Learning

Awesome interactive textbook on probability theory and statistics Inside are clear visualizations, interactive elements, and minimal dry theory. You can tweak distributions, sample datasets, play with confidence intervals, and clearly see how it all works Get it here, I recommend opening it on a desktop https://seeing-theory.brown.edu/ 👉 @DataScienceM I spent years chasing success until I found the 7 daily habits no one talks about—now everything’s changed for me. Most people miss the real secret. See what you’ve been overlooking: Success Tips 🔥 | InsideAds

68 469

Repost from Machine Learning

📌 How to Build a Genetic Algorithm from Scratch in Python 🗂 Category: DATA SCIENCE 🕒 Date: 2024-08-30 | ⏱️ Read time: 16 min read A complete walkthrough on how one can build a Genetic Algorithm from scratch in Python,…

68 469

Repost from Github Top Repositories

Python library RetinaFace for face detection and working with key points (eyes, nose, mouth) Supports face alignment, easily installed via pip install retina-face, and works based on deep models from the insightface project. An excellent tool for tasks in computer vision and face recognition. Usage examples:

from retinaface import RetinaFace

resp = RetinaFace.detect_faces("img1.jpg")
print(resp)

{
    "face_1": {
        "score": 0.9993440508842468,
        "facial_area": [155, 81, 434, 443],
        "landmarks": {
          "right_eye": [257.82974, 209.64787],
          "left_eye": [374.93427, 251.78687],
          "nose": [303.4773, 299.91144],
          "mouth_right": [228.37329, 338.73193],
          "mouth_left": [320.21982, 374.58798]
        }
  }
}

👉 @DataScienceN

68 469

Creating QR codes with Python in just a few lines of code Anyone can generate their own QR code for a link, text, or even Wi-Fi data. For this, the qrcode library and the PIL module are used

pip install qrcode pillow

import qrcode
from PIL import Image

data = input("Enter data for QR: ")
qr = qrcode.QRCode(version=3, box_size=8, border=4)
qr.add_data(data)
qr.make(fit=True)

image = qr.make_image(fill="black", back_color="aqua")
image.save("qr_code.png")
Image.open("qr_code.png")

The output is a ready QR code with any text or link. You can change colors, sizes, and style to fit your design 🙂 👉 https://t.me/CodeProgrammer

68 469

Repost from Machine Learning

📌 A Guide to Clustering Algorithms 🗂 Category: DATA SCIENCE 🕒 Date: 2024-09-06 | ⏱️ Read time: 6 min read An overview of clustering and the different families of clustering algorithms.

68 469

What if you could double your trading power—today? Start with just $200 at Elite Gold Trading, and get a $200 bonus from our partner broker, plus +20% on every future deposit. Don’t wait—join now and copy proven AI strategies in real time. Trade smarter, grow faster, and see real results. Get started here #ad InsideAds

68 469

Repost from Machine Learning

📌 Image Segmentation With K-Means Clustering 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-05 | ⏱️ Read time: 11 min read An introduction with Python

68 469

Google Collab notebooks to learn everything you need to master prompt engineering with Claude - from basic structure and role prompting to advanced techniques like few-shot learning, avoiding hallucinations, and tool use. Perfect interactive lessons to level up your AI skills Link: https://github.com/anthropics/courses/tree/master/prompt_engineering_interactive_tutorial/Anthropic%201P

68 469

Soon 😉

68 469

Think trading is all about luck or chasing fast money? The truth is much simpler: real results come from steady discipline, smart risk management, and total transparency. Every day, we post real trading results—no hype, just facts. Want to see what’s really possible? Discover today’s actual performance right here. Get honest insight—join Elite Gold Trading! #ad InsideAds

68 469

Our paid channel contains more than 9000 books and 500 paid courses With a $3 monthly subscription, you can cancel whenever you want https://t.me/+r_Tcx2c-oVU1OWNi

68 469

Harvard's "Advanced Algorithms" by Jelani Nelson 📽 Lecture Videos: https://youtube.com/playlist?list=PL2SOU6wwxB0uP4rJgf5ayhHWgw7akUWSf 📷 Lecture Notes: https://people.seas.harvard.edu/~cs224/fall14/lec.html

68 469

Today I am 3️⃣0️⃣ years old, I am excited to make more successes and achievements My previous year was full of exciting events and economic, political and programmatic noise, but I kept moving forward Best regards Eng. @HusseinSheikho 🔤

68 469

“Nobody believed you could grow small capital—until I saw this.” $1,000 turned into real profit before my eyes. The secret? Bonus fuel & copytrading with Elite Gold. Want proof? See how it’s actually done before the bonus ends. #ad InsideAds

68 469

“Nobody believed I could double my deposit safely… But inside Elite Gold Trading, we proved it: real people, real profits, real discipline. Want to see how our members avoid big losses and keep winning? 👉 Discover the secret inside #ad InsideAds

68 469

Repost from Machine Learning

📌 A Basic Introduction to Quantum GANs 🗂 Category: MACHINE LEARNING 🕒 Date: 2024-09-21 | ⏱️ Read time: 11 min read “Quantum computing just becomes vastly simpler once you take the physics out of it.”